Build your product with an AI-agent-driven SDLC
Infinity designs and builds software products using specialised AI agents across discovery, architecture, engineering, testing, security and release. Our engineers own the architecture, review changes, and approve production releases.
How AI changes the way we build your product
AI does more than generate code. We use specialised agents for bounded work across the delivery lifecycle, while engineers remain accountable for product decisions, architecture and release quality.
01
Product work, not just coding
Agents support discovery, requirements, architecture, implementation, testing, documentation and release work — so acceleration is applied to the whole product lifecycle, not only code generation.
02
Specialised agents, clear roles
Different agents handle planning, coding, validation, security, observability and release tasks with separate tools, permissions and acceptance criteria.
From product idea to production software
We take responsibility for delivering the product. AI agents accelerate bounded tasks inside our engineering process; your product still has human owners, acceptance criteria, tested releases and a clear operating model.
03
Independent verification
The agent that creates a change does not approve its own work. Builds, tests, code review, security checks and policy gates provide independent evidence before release.
04
Human ownership where it matters
AI can automate routine engineering work, but named engineers remain responsible for architecture, security exceptions, production releases and incident response.
What production-grade AI delivery requires
AI changes how engineering work is performed, but it does not remove the need for evidence, separation of duties, security boundaries and accountable ownership.
AI agents can propose requirements, architecture, code and tests. Repositories, CI/CD, security controls and named reviewers determine what is accepted and what reaches production.
Evidence before release
Acceptance criteria → change → tests → review → release
AI agents can propose requirements, architecture, code and tests. Repositories, CI/CD, security controls and named reviewers determine what is accepted and what reaches production.
Evidence before release
Acceptance criteria → change → tests → review → release
60%
Average Team Velocity Growth
Using AI agents we achieve up to 60% velocity growth of our Engineering teams
Agents that create code do not approve their own work. Validation, security checks and release decisions are handled by separate controls and accountable reviewers.
Agents that create code do not approve their own work. Validation, security checks and release decisions are handled by separate controls and accountable reviewers.
Sensitive code and data are routed only to approved environments and models. Capability, privacy, residency, latency and cost are explicit engineering constraints.
80%
Cost Reduction
For new product development
Sensitive code and data are routed only to approved environments and models. Capability, privacy, residency, latency and cost are explicit engineering constraints.
How InfinitySDLC works across the lifecycle
InfinitySDLC applies specialised agents and deterministic controls across the delivery lifecycle. Each stage produces evidence for the next one, while engineers remain accountable for architecture, acceptance and production release.
Product Discovery & Requirements
Agents help turn goals, user evidence and constraints into structured requirements, acceptance criteria, edge cases and a traceable product backlog.
Planning & Architecture
Architecture agents analyse dependencies, interfaces, data flows, non-functional requirements and delivery risks before implementation begins.
Environment & Tooling
Repositories, environments, CI/CD, tool access and policy boundaries are prepared so agents work inside controlled engineering contexts rather than unrestricted workspaces.
AI-Assisted Engineering
Developer agents support implementation, refactoring, migrations, documentation and infrastructure changes in small, reviewable increments tied back to the original task.
QA & Validation
Automated builds, tests, regression checks, code-quality analysis and independent review verify the change against acceptance criteria before it progresses.
Security & Release Controls
Security checks, dependency and secret scanning, policy gates, approvals and release orchestration keep high-risk changes under explicit control.
Observability & Reliability
Telemetry, incidents, user feedback and delivery outcomes feed back into tests, documentation and engineering decisions so the product can improve safely after release.
Model Routing & Governance
Tasks are routed to approved models based on capability, privacy, residency, latency and cost, while high-impact decisions remain within defined human authority.
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Roman Reznikov
CEO & Partner
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